Relationships for three-dimensional modeling of co-channel reuse

Porter, P.T.

doi:10.1109/t-vt.1985.24038

Cited by 18 publications

(2 citation statements)

References 8 publications

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“…The difference between (13), (14) and (15) and standard competitive networks is that the maximum is not taken over all units in the layer. Instead, it is taken over fl.…”

Section: Se Flmentioning

confidence: 99%

“…Units are assumed to in one of two possible states described by a transmission commitment variable c5 E (0, 1) for a wireless unit at position s: a committed state, that is, c5 = 1, where the unit s competes with its neighboring units and can become active, and an uncommitted State, c =0, where the unit never becomes active in the neighborhood. Thus, the righthand terms in activation rules (13), (14), and (15) are multiplied by c5(t) to yield the complete activation rule. Nodes may transition between committed and uncommitted states.…”

Section: Application To the Setf-organizin Wireless Network Constructmentioning

confidence: 99%

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<title>Application of neural networks to the dynamic spatial distribution of nodes within an urban wireless network</title>

Hortos

1995

Applications and Science of Artificial Neural Networks

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The optimal location of wireless transceivers or communicating sensor devices in an urban area and within large human-made structures is considered. The purpose of the positioning the devices is formation of a distributed network, either in a mesh or hub-spoke topology, that achieves robust connectivity of the nodes. Real-world examples include wireless local area networks (LANs) within buildings and radio beacons in an outdoor mobile radio environment. Operating environments contain both fixed and moving interferers that correspond to both stationary and time-varying spatial distributions of path distortion of stationary and transient fading and multipath delays that impede connectivity.The positioning of the autonomous wireless devices in an area with an unknown spatial pattern of interferers would be normallybe a slow incremental process. The proposed objective is determination of the spatial distribution of the devices to achieve the maximum radio connectivity in a minimal number of iterative steps. Impeding the optimal distribution of wireless nodes is the corresponding distribution of environmental interferers in the area or volume of network operation. The problem of network formation is posed as an adaptive learning problem, in particular, a self-organizing map of locally competitive wireless units that recursively update their positions and individual operating configurations at each iterative step of the neural algorithm. The scheme allows the wireless units to adaptively learn the pattern distribution of interferers in their operating environment based on the level of radio interference measured at each node by an equivalent received signal strength from wireless units within the node's hearing distance.Two cases are considered. The first is an indoor human-made environment where the interference pattern is largely deterministic and stationary and the units are positioned to form a wireless LAN. The second situation applies to an outdoor urban environment, where a fixed number of units on mobile platforms operating in a random spatial distribution of interferers.

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“…The difference between (13), (14) and (15) and standard competitive networks is that the maximum is not taken over all units in the layer. Instead, it is taken over fl.…”

Section: Se Flmentioning

confidence: 99%

Section: Application To the Setf-organizin Wireless Network Constructmentioning

confidence: 99%